Overview

Dataset statistics

Number of variables22
Number of observations1930
Missing cells17853
Missing cells (%)42.0%
Duplicate rows13
Duplicate rows (%)0.7%
Total size in memory346.8 KiB
Average record size in memory184.0 B

Variable types

Text2
Unsupported1
Categorical5
Numeric14

Alerts

Region has constant value ""Constant
Division has constant value ""Constant
Games_Level has constant value ""Constant
Qualifier has constant value ""Constant
Dataset has 13 (0.7%) duplicate rowsDuplicates
Back Squat (lbs) is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Chad1000x (s) is highly overall correlated with Filthy 50 (s) and 1 other fieldsHigh correlation
Clean and Jerk (lbs) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Deadlift (lbs) is highly overall correlated with Back Squat (lbs) and 4 other fieldsHigh correlation
Fight Gone Bad is highly overall correlated with Filthy 50 (s) and 2 other fieldsHigh correlation
Filthy 50 (s) is highly overall correlated with Chad1000x (s) and 4 other fieldsHigh correlation
Fran (s) is highly overall correlated with Clean and Jerk (lbs) and 7 other fieldsHigh correlation
Grace (s) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Helen (s) is highly overall correlated with Filthy 50 (s) and 5 other fieldsHigh correlation
L1 Benchmark (s) is highly overall correlated with Back Squat (lbs) and 11 other fieldsHigh correlation
Max Pull-ups is highly overall correlated with Fran (s) and 2 other fieldsHigh correlation
Rank is highly overall correlated with Clean and Jerk (lbs) and 4 other fieldsHigh correlation
Run 5k (s) is highly overall correlated with Helen (s) and 2 other fieldsHigh correlation
Snatch (lbs) is highly overall correlated with Back Squat (lbs) and 6 other fieldsHigh correlation
Sprint 400m (s) is highly overall correlated with Chad1000x (s) and 3 other fieldsHigh correlation
Affiliate has 165 (8.5%) missing valuesMissing
Country has 1930 (100.0%) missing valuesMissing
Back Squat (lbs) has 149 (7.7%) missing valuesMissing
Clean and Jerk (lbs) has 157 (8.1%) missing valuesMissing
Deadlift (lbs) has 131 (6.8%) missing valuesMissing
Snatch (lbs) has 204 (10.6%) missing valuesMissing
Fight Gone Bad has 1540 (79.8%) missing valuesMissing
Max Pull-ups has 1329 (68.9%) missing valuesMissing
Chad1000x (s) has 1906 (98.8%) missing valuesMissing
L1 Benchmark (s) has 1927 (99.8%) missing valuesMissing
Filthy 50 (s) has 1700 (88.1%) missing valuesMissing
Fran (s) has 1102 (57.1%) missing valuesMissing
Grace (s) has 1178 (61.0%) missing valuesMissing
Helen (s) has 1440 (74.6%) missing valuesMissing
Run 5k (s) has 1366 (70.8%) missing valuesMissing
Sprint 400m (s) has 1629 (84.4%) missing valuesMissing
L1 Benchmark (s) is uniformly distributedUniform
Country is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-17 20:24:45.096692
Analysis finished2024-02-17 20:25:06.762587
Duration21.67 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct1917
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:06.907789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length14.095337
Min length7

Characters and Unicode

Total characters27204
Distinct characters73
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1904 ?
Unique (%)98.7%

Sample

1st rowAndrea Nisler
2nd rowChloe Gauvin-David
3rd rowEmelie Lundberg
4th rowJessica Coughlan
5th rowAstrid Tind Petersen
ValueCountFrequency (%)
jessica 33
 
0.8%
sarah 31
 
0.8%
lauren 29
 
0.7%
emily 24
 
0.6%
anna 21
 
0.5%
samantha 20
 
0.5%
amanda 19
 
0.5%
ashley 19
 
0.5%
nicole 18
 
0.5%
taylor 18
 
0.5%
Other values (2649) 3726
94.1%
2024-02-17T15:25:07.241696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2986
 
11.0%
e 2756
 
10.1%
2028
 
7.5%
n 1977
 
7.3%
i 1866
 
6.9%
r 1704
 
6.3%
l 1550
 
5.7%
o 1230
 
4.5%
s 1066
 
3.9%
t 967
 
3.6%
Other values (63) 9074
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21118
77.6%
Uppercase Letter 4018
 
14.8%
Space Separator 2028
 
7.5%
Dash Punctuation 31
 
0.1%
Other Punctuation 7
 
< 0.1%
Final Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2986
14.1%
e 2756
13.1%
n 1977
9.4%
i 1866
8.8%
r 1704
8.1%
l 1550
 
7.3%
o 1230
 
5.8%
s 1066
 
5.0%
t 967
 
4.6%
h 758
 
3.6%
Other values (30) 4258
20.2%
Uppercase Letter
ValueCountFrequency (%)
S 416
 
10.4%
M 390
 
9.7%
C 366
 
9.1%
A 314
 
7.8%
K 268
 
6.7%
B 260
 
6.5%
L 220
 
5.5%
J 212
 
5.3%
H 210
 
5.2%
R 191
 
4.8%
Other values (18) 1171
29.1%
Other Punctuation
ValueCountFrequency (%)
' 5
71.4%
. 2
 
28.6%
Space Separator
ValueCountFrequency (%)
2028
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25136
92.4%
Common 2068
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2986
 
11.9%
e 2756
 
11.0%
n 1977
 
7.9%
i 1866
 
7.4%
r 1704
 
6.8%
l 1550
 
6.2%
o 1230
 
4.9%
s 1066
 
4.2%
t 967
 
3.8%
h 758
 
3.0%
Other values (58) 8276
32.9%
Common
ValueCountFrequency (%)
2028
98.1%
- 31
 
1.5%
' 5
 
0.2%
. 2
 
0.1%
2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27152
99.8%
None 50
 
0.2%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2986
 
11.0%
e 2756
 
10.2%
2028
 
7.5%
n 1977
 
7.3%
i 1866
 
6.9%
r 1704
 
6.3%
l 1550
 
5.7%
o 1230
 
4.5%
s 1066
 
3.9%
t 967
 
3.6%
Other values (46) 9022
33.2%
None
ValueCountFrequency (%)
é 8
16.0%
í 8
16.0%
ó 7
14.0%
á 5
10.0%
ä 4
8.0%
ø 3
 
6.0%
å 3
 
6.0%
ö 3
 
6.0%
ü 2
 
4.0%
æ 1
 
2.0%
Other values (6) 6
12.0%
Punctuation
ValueCountFrequency (%)
2
100.0%

Affiliate
Text

MISSING 

Distinct1420
Distinct (%)80.5%
Missing165
Missing (%)8.5%
Memory size30.2 KiB
2024-02-17T15:25:07.493864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length17.307649
Min length10

Characters and Unicode

Total characters30548
Distinct characters81
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1148 ?
Unique (%)65.0%

Sample

1st rowCrossFit East Nashville
2nd rowCrossFit Move Fast Lift Heavy
3rd rowCrossFit OBA
4th rowCrossFit NorWest
5th rowCrossFit Butcher's Lab
ValueCountFrequency (%)
crossfit 1765
42.2%
city 31
 
0.7%
iron 13
 
0.3%
north 13
 
0.3%
east 12
 
0.3%
athletics 12
 
0.3%
south 12
 
0.3%
west 11
 
0.3%
the 10
 
0.2%
valley 10
 
0.2%
Other values (1609) 2296
54.9%
2024-02-17T15:25:07.876244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 4104
13.4%
o 2676
 
8.8%
r 2664
 
8.7%
i 2593
 
8.5%
t 2572
 
8.4%
2420
 
7.9%
C 2016
 
6.6%
F 1888
 
6.2%
e 1336
 
4.4%
a 1109
 
3.6%
Other values (71) 7170
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21523
70.5%
Uppercase Letter 6177
 
20.2%
Space Separator 2420
 
7.9%
Decimal Number 386
 
1.3%
Other Punctuation 27
 
0.1%
Dash Punctuation 14
 
< 0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4104
19.1%
o 2676
12.4%
r 2664
12.4%
i 2593
12.0%
t 2572
12.0%
e 1336
 
6.2%
a 1109
 
5.2%
n 829
 
3.9%
l 662
 
3.1%
u 427
 
2.0%
Other values (27) 2551
11.9%
Uppercase Letter
ValueCountFrequency (%)
C 2016
32.6%
F 1888
30.6%
S 248
 
4.0%
B 198
 
3.2%
A 165
 
2.7%
T 160
 
2.6%
R 146
 
2.4%
P 137
 
2.2%
M 136
 
2.2%
L 124
 
2.0%
Other values (18) 959
15.5%
Decimal Number
ValueCountFrequency (%)
1 73
18.9%
0 55
14.2%
4 46
11.9%
2 42
10.9%
9 36
9.3%
6 31
8.0%
5 29
 
7.5%
8 28
 
7.3%
7 24
 
6.2%
3 22
 
5.7%
Other Punctuation
ValueCountFrequency (%)
' 16
59.3%
. 10
37.0%
& 1
 
3.7%
Space Separator
ValueCountFrequency (%)
2420
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27700
90.7%
Common 2848
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4104
14.8%
o 2676
9.7%
r 2664
9.6%
i 2593
9.4%
t 2572
9.3%
C 2016
 
7.3%
F 1888
 
6.8%
e 1336
 
4.8%
a 1109
 
4.0%
n 829
 
3.0%
Other values (55) 5913
21.3%
Common
ValueCountFrequency (%)
2420
85.0%
1 73
 
2.6%
0 55
 
1.9%
4 46
 
1.6%
2 42
 
1.5%
9 36
 
1.3%
6 31
 
1.1%
5 29
 
1.0%
8 28
 
1.0%
7 24
 
0.8%
Other values (6) 64
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30514
99.9%
None 33
 
0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4104
13.4%
o 2676
 
8.8%
r 2664
 
8.7%
i 2593
 
8.5%
t 2572
 
8.4%
2420
 
7.9%
C 2016
 
6.6%
F 1888
 
6.2%
e 1336
 
4.4%
a 1109
 
3.6%
Other values (57) 7136
23.4%
None
ValueCountFrequency (%)
ä 8
24.2%
í 5
15.2%
ü 4
12.1%
á 3
 
9.1%
ø 3
 
9.1%
é 2
 
6.1%
ã 2
 
6.1%
ô 1
 
3.0%
ð 1
 
3.0%
Á 1
 
3.0%
Other values (3) 3
 
9.1%
Punctuation
ValueCountFrequency (%)
1
100.0%

Country
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1930
Missing (%)100.0%
Memory size30.2 KiB

Region
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
worldwide
1930 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters17370
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 1930
100.0%

Length

2024-02-17T15:25:08.019006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:25:08.107938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 1930
100.0%

Most occurring characters

ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17370
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 17370
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Division
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
Women
1930 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters9650
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWomen
2nd rowWomen
3rd rowWomen
4th rowWomen
5th rowWomen

Common Values

ValueCountFrequency (%)
Women 1930
100.0%

Length

2024-02-17T15:25:08.200068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:25:08.284818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
women 1930
100.0%

Most occurring characters

ValueCountFrequency (%)
W 1930
20.0%
o 1930
20.0%
m 1930
20.0%
e 1930
20.0%
n 1930
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7720
80.0%
Uppercase Letter 1930
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1930
25.0%
m 1930
25.0%
e 1930
25.0%
n 1930
25.0%
Uppercase Letter
ValueCountFrequency (%)
W 1930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9650
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 1930
20.0%
o 1930
20.0%
m 1930
20.0%
e 1930
20.0%
n 1930
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 1930
20.0%
o 1930
20.0%
m 1930
20.0%
e 1930
20.0%
n 1930
20.0%

Rank
Real number (ℝ)

HIGH CORRELATION 

Distinct1911
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27566.008
Minimum17
Maximum123110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:08.390020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile1481.7
Q110742
median20271
Q338398
95-th percentile80215.4
Maximum123110
Range123093
Interquartile range (IQR)27656

Descriptive statistics

Standard deviation23742.001
Coefficient of variation (CV)0.86127819
Kurtosis1.39221
Mean27566.008
Median Absolute Deviation (MAD)12630
Skewness1.3158758
Sum53202395
Variance5.6368263 × 108
MonotonicityIncreasing
2024-02-17T15:25:08.530411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14108 3
 
0.2%
45129 2
 
0.1%
57116 2
 
0.1%
17557 2
 
0.1%
24034 2
 
0.1%
22765 2
 
0.1%
80450 2
 
0.1%
42715 2
 
0.1%
83206 2
 
0.1%
49355 2
 
0.1%
Other values (1901) 1909
98.9%
ValueCountFrequency (%)
17 1
0.1%
37 1
0.1%
51 1
0.1%
55 1
0.1%
105 1
0.1%
118 1
0.1%
140 1
0.1%
142 1
0.1%
147 1
0.1%
153 1
0.1%
ValueCountFrequency (%)
123110 1
0.1%
121746 1
0.1%
115832 1
0.1%
113774 1
0.1%
112806 1
0.1%
112296 1
0.1%
111358 1
0.1%
109949 1
0.1%
109187 1
0.1%
108671 1
0.1%

Games_Level
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
worldwide
1930 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters17370
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworldwide
2nd rowworldwide
3rd rowworldwide
4th rowworldwide
5th rowworldwide

Common Values

ValueCountFrequency (%)
worldwide 1930
100.0%

Length

2024-02-17T15:25:08.648401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:25:08.733850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
worldwide 1930
100.0%

Most occurring characters

ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17370
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 17370
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 3860
22.2%
d 3860
22.2%
o 1930
11.1%
r 1930
11.1%
l 1930
11.1%
i 1930
11.1%
e 1930
11.1%

Qualifier
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
open
1930 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters7720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowopen
2nd rowopen
3rd rowopen
4th rowopen
5th rowopen

Common Values

ValueCountFrequency (%)
open 1930
100.0%

Length

2024-02-17T15:25:08.823142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:25:08.907365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
open 1930
100.0%

Most occurring characters

ValueCountFrequency (%)
o 1930
25.0%
p 1930
25.0%
e 1930
25.0%
n 1930
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7720
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1930
25.0%
p 1930
25.0%
e 1930
25.0%
n 1930
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7720
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1930
25.0%
p 1930
25.0%
e 1930
25.0%
n 1930
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1930
25.0%
p 1930
25.0%
e 1930
25.0%
n 1930
25.0%

Back Squat (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct178
Distinct (%)10.0%
Missing149
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean230.52578
Minimum80
Maximum410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:09.010110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile160
Q1200
median229.28048
Q3260
95-th percentile308
Maximum410
Range330
Interquartile range (IQR)60

Descriptive statistics

Standard deviation45.339704
Coefficient of variation (CV)0.19667953
Kurtosis0.23825097
Mean230.52578
Median Absolute Deviation (MAD)30.71952
Skewness0.12790886
Sum410566.42
Variance2055.6887
MonotonicityNot monotonic
2024-02-17T15:25:09.144517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 89
 
4.6%
245 67
 
3.5%
235 63
 
3.3%
215 60
 
3.1%
205 59
 
3.1%
220.462 52
 
2.7%
250 48
 
2.5%
200 48
 
2.5%
265 48
 
2.5%
210 45
 
2.3%
Other values (168) 1202
62.3%
(Missing) 149
 
7.7%
ValueCountFrequency (%)
80 2
0.1%
85 1
 
0.1%
90 1
 
0.1%
95 1
 
0.1%
100 1
 
0.1%
105 3
0.2%
110 1
 
0.1%
115 1
 
0.1%
116.84486 2
0.1%
121.2541 1
 
0.1%
ValueCountFrequency (%)
410 1
 
0.1%
396.8316 1
 
0.1%
385 1
 
0.1%
370 1
 
0.1%
365 1
 
0.1%
360 1
 
0.1%
355 3
0.2%
352 2
0.1%
350 3
0.2%
345 1
 
0.1%

Clean and Jerk (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct156
Distinct (%)8.8%
Missing157
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean168.91863
Minimum65
Maximum297.6237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:09.276457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile120
Q1145
median165.3465
Q3190
95-th percentile225
Maximum297.6237
Range232.6237
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.270369
Coefficient of variation (CV)0.1910409
Kurtosis0.049958504
Mean168.91863
Median Absolute Deviation (MAD)22.0462
Skewness0.18497966
Sum299492.74
Variance1041.3767
MonotonicityNot monotonic
2024-02-17T15:25:09.404765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 103
 
5.3%
175 98
 
5.1%
155 91
 
4.7%
145 79
 
4.1%
135 74
 
3.8%
185 70
 
3.6%
195 59
 
3.1%
170 58
 
3.0%
180 55
 
2.8%
205 51
 
2.6%
Other values (146) 1035
53.6%
(Missing) 157
 
8.1%
ValueCountFrequency (%)
65 1
 
0.1%
70 1
 
0.1%
77.1617 1
 
0.1%
80 1
 
0.1%
81.57094 1
 
0.1%
85 4
0.2%
88.1848 1
 
0.1%
90 4
0.2%
93 1
 
0.1%
94.79866 1
 
0.1%
ValueCountFrequency (%)
297.6237 1
 
0.1%
285 1
 
0.1%
275 1
 
0.1%
272 1
 
0.1%
265 1
 
0.1%
260 2
 
0.1%
257 1
 
0.1%
253.5313 1
 
0.1%
250 5
0.3%
246 1
 
0.1%

Deadlift (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct176
Distinct (%)9.8%
Missing131
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean279.0357
Minimum8.81848
Maximum440.924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:09.534585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.81848
5-th percentile205
Q1245
median275.5775
Q3310.92571
95-th percentile360
Maximum440.924
Range432.10552
Interquartile range (IQR)65.92571

Descriptive statistics

Standard deviation49.359578
Coefficient of variation (CV)0.17689341
Kurtosis0.7182181
Mean279.0357
Median Absolute Deviation (MAD)33.0693
Skewness-0.069928723
Sum501985.23
Variance2436.3679
MonotonicityNot monotonic
2024-02-17T15:25:09.663804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 90
 
4.7%
265 77
 
4.0%
305 67
 
3.5%
315 65
 
3.4%
245 65
 
3.4%
275 63
 
3.3%
285 60
 
3.1%
286.6006 50
 
2.6%
225 48
 
2.5%
335 45
 
2.3%
Other values (166) 1169
60.6%
(Missing) 131
 
6.8%
ValueCountFrequency (%)
8.81848 1
0.1%
100 1
0.1%
105 2
0.1%
110 1
0.1%
120 1
0.1%
125 1
0.1%
132.2772 1
0.1%
135 2
0.1%
143 1
0.1%
143.3003 1
0.1%
ValueCountFrequency (%)
440.924 1
 
0.1%
435 1
 
0.1%
425 2
 
0.1%
420 1
 
0.1%
418.8778 1
 
0.1%
415 2
 
0.1%
410 1
 
0.1%
407.8547 1
 
0.1%
405 7
0.4%
403.44546 1
 
0.1%

Snatch (lbs)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct146
Distinct (%)8.5%
Missing204
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean129.44184
Minimum40
Maximum231.4851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:09.786904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile85
Q1110
median130
Q3147.70954
95-th percentile176.8424
Maximum231.4851
Range191.4851
Interquartile range (IQR)37.70954

Descriptive statistics

Standard deviation28.267916
Coefficient of variation (CV)0.21838314
Kurtosis0.015084555
Mean129.44184
Median Absolute Deviation (MAD)20
Skewness0.19102519
Sum223416.61
Variance799.07506
MonotonicityNot monotonic
2024-02-17T15:25:09.924136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 96
 
5.0%
125 90
 
4.7%
105 87
 
4.5%
115 86
 
4.5%
145 70
 
3.6%
110 66
 
3.4%
120 64
 
3.3%
140 63
 
3.3%
130 61
 
3.2%
155 57
 
3.0%
Other values (136) 986
51.1%
(Missing) 204
 
10.6%
ValueCountFrequency (%)
40 1
 
0.1%
44.0924 1
 
0.1%
45 2
0.1%
50 1
 
0.1%
55 3
0.2%
55.1155 1
 
0.1%
60 1
 
0.1%
65 3
0.2%
66.1386 4
0.2%
69 2
0.1%
ValueCountFrequency (%)
231.4851 1
 
0.1%
225 1
 
0.1%
220 1
 
0.1%
215 2
0.1%
210 2
0.1%
205 3
0.2%
202.82504 1
 
0.1%
202 1
 
0.1%
200.62042 1
 
0.1%
200 2
0.1%

Fight Gone Bad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct182
Distinct (%)46.7%
Missing1540
Missing (%)79.8%
Infinite0
Infinite (%)0.0%
Mean292.33846
Minimum136
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:10.053979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum136
5-th percentile195.35
Q1255.25
median289
Q3326
95-th percentile394
Maximum730
Range594
Interquartile range (IQR)70.75

Descriptive statistics

Standard deviation62.705115
Coefficient of variation (CV)0.21449492
Kurtosis5.6254003
Mean292.33846
Median Absolute Deviation (MAD)36
Skewness0.94720191
Sum114012
Variance3931.9314
MonotonicityNot monotonic
2024-02-17T15:25:10.183315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321 8
 
0.4%
308 7
 
0.4%
300 6
 
0.3%
270 6
 
0.3%
284 6
 
0.3%
326 6
 
0.3%
268 6
 
0.3%
279 6
 
0.3%
288 5
 
0.3%
273 5
 
0.3%
Other values (172) 329
 
17.0%
(Missing) 1540
79.8%
ValueCountFrequency (%)
136 1
0.1%
150 1
0.1%
153 1
0.1%
154 1
0.1%
156 1
0.1%
158 1
0.1%
165 1
0.1%
167 1
0.1%
173 1
0.1%
177 1
0.1%
ValueCountFrequency (%)
730 1
0.1%
473 1
0.1%
464 1
0.1%
462 1
0.1%
461 1
0.1%
430 1
0.1%
424 1
0.1%
415 1
0.1%
413 1
0.1%
412 1
0.1%

Max Pull-ups
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)10.0%
Missing1329
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean22.459235
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:10.315667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q112
median21
Q330
95-th percentile48
Maximum100
Range99
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.734461
Coefficient of variation (CV)0.61152845
Kurtosis1.9400377
Mean22.459235
Median Absolute Deviation (MAD)9
Skewness0.99955274
Sum13498
Variance188.63542
MonotonicityNot monotonic
2024-02-17T15:25:10.447936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 49
 
2.5%
30 47
 
2.4%
10 42
 
2.2%
25 40
 
2.1%
15 35
 
1.8%
12 29
 
1.5%
40 24
 
1.2%
21 22
 
1.1%
5 19
 
1.0%
27 14
 
0.7%
Other values (50) 280
 
14.5%
(Missing) 1329
68.9%
ValueCountFrequency (%)
1 9
 
0.5%
2 7
 
0.4%
3 13
 
0.7%
4 7
 
0.4%
5 19
1.0%
6 9
 
0.5%
7 6
 
0.3%
8 12
 
0.6%
9 6
 
0.3%
10 42
2.2%
ValueCountFrequency (%)
100 1
 
0.1%
75 1
 
0.1%
73 1
 
0.1%
65 2
 
0.1%
62 1
 
0.1%
60 5
0.3%
57 2
 
0.1%
56 1
 
0.1%
55 3
0.2%
52 1
 
0.1%

Chad1000x (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)95.8%
Missing1906
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean3948.7917
Minimum2573
Maximum6096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:10.671124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2573
5-th percentile3072.25
Q13414
median3883
Q34388.25
95-th percentile4778.4
Maximum6096
Range3523
Interquartile range (IQR)974.25

Descriptive statistics

Standard deviation749.16382
Coefficient of variation (CV)0.18971976
Kurtosis1.5871075
Mean3948.7917
Median Absolute Deviation (MAD)495
Skewness0.81122045
Sum94771
Variance561246.43
MonotonicityNot monotonic
2024-02-17T15:25:10.823607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4338 2
 
0.1%
2573 1
 
0.1%
3366 1
 
0.1%
3621 1
 
0.1%
3579 1
 
0.1%
3939 1
 
0.1%
4485 1
 
0.1%
3600 1
 
0.1%
3170 1
 
0.1%
3840 1
 
0.1%
Other values (13) 13
 
0.7%
(Missing) 1906
98.8%
ValueCountFrequency (%)
2573 1
0.1%
3055 1
0.1%
3170 1
0.1%
3242 1
0.1%
3344 1
0.1%
3366 1
0.1%
3430 1
0.1%
3579 1
0.1%
3600 1
0.1%
3621 1
0.1%
ValueCountFrequency (%)
6096 1
0.1%
4782 1
0.1%
4758 1
0.1%
4670 1
0.1%
4629 1
0.1%
4485 1
0.1%
4356 1
0.1%
4338 2
0.1%
3939 1
0.1%
3928 1
0.1%

L1 Benchmark (s)
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing1927
Missing (%)99.8%
Memory size30.2 KiB
244.0
280.0
360.0

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row244.0
2nd row280.0
3rd row360.0

Common Values

ValueCountFrequency (%)
244.0 1
 
0.1%
280.0 1
 
0.1%
360.0 1
 
0.1%
(Missing) 1927
99.8%

Length

2024-02-17T15:25:10.952115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-17T15:25:11.053491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
244.0 1
33.3%
280.0 1
33.3%
360.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
0 5
33.3%
. 3
20.0%
2 2
 
13.3%
4 2
 
13.3%
8 1
 
6.7%
3 1
 
6.7%
6 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
80.0%
Other Punctuation 3
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5
41.7%
2 2
 
16.7%
4 2
 
16.7%
8 1
 
8.3%
3 1
 
8.3%
6 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5
33.3%
. 3
20.0%
2 2
 
13.3%
4 2
 
13.3%
8 1
 
6.7%
3 1
 
6.7%
6 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5
33.3%
. 3
20.0%
2 2
 
13.3%
4 2
 
13.3%
8 1
 
6.7%
3 1
 
6.7%
6 1
 
6.7%

Filthy 50 (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct212
Distinct (%)92.2%
Missing1700
Missing (%)88.1%
Infinite0
Infinite (%)0.0%
Mean1598.1304
Minimum720
Maximum3048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:11.167682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum720
5-th percentile1120.95
Q11342.25
median1562
Q31799.5
95-th percentile2130.75
Maximum3048
Range2328
Interquartile range (IQR)457.25

Descriptive statistics

Standard deviation341.5354
Coefficient of variation (CV)0.21370934
Kurtosis2.2123092
Mean1598.1304
Median Absolute Deviation (MAD)225.5
Skewness0.91491492
Sum367570
Variance116646.43
MonotonicityNot monotonic
2024-02-17T15:25:11.295335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1384 2
 
0.1%
1332 2
 
0.1%
1413 2
 
0.1%
1598 2
 
0.1%
1984 2
 
0.1%
1562 2
 
0.1%
1685 2
 
0.1%
1535 2
 
0.1%
1800 2
 
0.1%
1818 2
 
0.1%
Other values (202) 210
 
10.9%
(Missing) 1700
88.1%
ValueCountFrequency (%)
720 1
0.1%
908 1
0.1%
1009 1
0.1%
1056 1
0.1%
1061 1
0.1%
1063 1
0.1%
1075 1
0.1%
1088 1
0.1%
1097 1
0.1%
1109 1
0.1%
ValueCountFrequency (%)
3048 1
0.1%
2987 1
0.1%
2878 1
0.1%
2400 1
0.1%
2330 1
0.1%
2318 1
0.1%
2300 1
0.1%
2266 1
0.1%
2220 1
0.1%
2194 1
0.1%

Fran (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct349
Distinct (%)42.1%
Missing1102
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean298.18237
Minimum114
Maximum858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:11.416237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile149
Q1210.75
median280
Q3360
95-th percentile513.6
Maximum858
Range744
Interquartile range (IQR)149.25

Descriptive statistics

Standard deviation116.07281
Coefficient of variation (CV)0.38926784
Kurtosis1.7085218
Mean298.18237
Median Absolute Deviation (MAD)73
Skewness1.0875543
Sum246895
Variance13472.897
MonotonicityNot monotonic
2024-02-17T15:25:11.553552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 10
 
0.5%
290 7
 
0.4%
317 7
 
0.4%
267 7
 
0.4%
285 7
 
0.4%
346 7
 
0.4%
165 6
 
0.3%
238 6
 
0.3%
432 6
 
0.3%
341 6
 
0.3%
Other values (339) 759
39.3%
(Missing) 1102
57.1%
ValueCountFrequency (%)
114 1
 
0.1%
115 1
 
0.1%
123 1
 
0.1%
125 1
 
0.1%
126 1
 
0.1%
128 3
0.2%
129 1
 
0.1%
130 1
 
0.1%
131 2
0.1%
132 2
0.1%
ValueCountFrequency (%)
858 1
0.1%
832 1
0.1%
764 1
0.1%
748 1
0.1%
694 1
0.1%
673 1
0.1%
671 1
0.1%
668 2
0.1%
663 1
0.1%
657 1
0.1%

Grace (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct255
Distinct (%)33.9%
Missing1178
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean192.37766
Minimum32
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:11.688983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile102
Q1138
median173
Q3225.25
95-th percentile330.9
Maximum900
Range868
Interquartile range (IQR)87.25

Descriptive statistics

Standard deviation84.9877
Coefficient of variation (CV)0.44177531
Kurtosis13.585869
Mean192.37766
Median Absolute Deviation (MAD)43
Skewness2.6239979
Sum144668
Variance7222.9091
MonotonicityNot monotonic
2024-02-17T15:25:11.816448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144 12
 
0.6%
137 12
 
0.6%
157 10
 
0.5%
189 9
 
0.5%
170 8
 
0.4%
169 8
 
0.4%
118 8
 
0.4%
173 8
 
0.4%
156 8
 
0.4%
165 8
 
0.4%
Other values (245) 661
34.2%
(Missing) 1178
61.0%
ValueCountFrequency (%)
32 1
0.1%
67 1
0.1%
70 2
0.1%
74 1
0.1%
75 1
0.1%
77 1
0.1%
78 1
0.1%
79 1
0.1%
81 2
0.1%
84 1
0.1%
ValueCountFrequency (%)
900 1
0.1%
833 1
0.1%
660 1
0.1%
645 1
0.1%
604 1
0.1%
526 1
0.1%
521 1
0.1%
519 1
0.1%
510 1
0.1%
486 1
0.1%

Helen (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct283
Distinct (%)57.8%
Missing1440
Missing (%)74.6%
Infinite0
Infinite (%)0.0%
Mean658.35918
Minimum300
Maximum1920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:11.938998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile498.45
Q1569.25
median631.5
Q3714.5
95-th percentile895.85
Maximum1920
Range1620
Interquartile range (IQR)145.25

Descriptive statistics

Standard deviation153.75575
Coefficient of variation (CV)0.23354387
Kurtosis21.117472
Mean658.35918
Median Absolute Deviation (MAD)72.5
Skewness3.2942237
Sum322596
Variance23640.832
MonotonicityNot monotonic
2024-02-17T15:25:12.081244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
585 7
 
0.4%
637 6
 
0.3%
558 6
 
0.3%
704 5
 
0.3%
783 5
 
0.3%
653 5
 
0.3%
588 5
 
0.3%
518 5
 
0.3%
667 5
 
0.3%
576 4
 
0.2%
Other values (273) 437
 
22.6%
(Missing) 1440
74.6%
ValueCountFrequency (%)
300 1
0.1%
397 1
0.1%
430 1
0.1%
435 1
0.1%
441 1
0.1%
453 1
0.1%
455 1
0.1%
459 1
0.1%
461 1
0.1%
463 1
0.1%
ValueCountFrequency (%)
1920 2
0.1%
1623 1
0.1%
1223 1
0.1%
1192 1
0.1%
1129 1
0.1%
1092 1
0.1%
1075 1
0.1%
1062 1
0.1%
1051 1
0.1%
1015 1
0.1%

Run 5k (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct346
Distinct (%)61.3%
Missing1366
Missing (%)70.8%
Infinite0
Infinite (%)0.0%
Mean1594.9131
Minimum1020
Maximum3366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:12.218428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1241
Q11395.75
median1560
Q31713
95-th percentile2149.3
Maximum3366
Range2346
Interquartile range (IQR)317.25

Descriptive statistics

Standard deviation294.59955
Coefficient of variation (CV)0.18471197
Kurtosis4.3120581
Mean1594.9131
Median Absolute Deviation (MAD)159
Skewness1.515573
Sum899531
Variance86788.893
MonotonicityNot monotonic
2024-02-17T15:25:12.344520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1620 13
 
0.7%
1500 11
 
0.6%
1680 9
 
0.5%
1440 9
 
0.5%
1260 8
 
0.4%
1560 8
 
0.4%
1800 8
 
0.4%
1380 7
 
0.4%
1320 6
 
0.3%
1350 6
 
0.3%
Other values (336) 479
 
24.8%
(Missing) 1366
70.8%
ValueCountFrequency (%)
1020 1
0.1%
1040 1
0.1%
1063 1
0.1%
1070 1
0.1%
1128 1
0.1%
1140 1
0.1%
1148 1
0.1%
1160 1
0.1%
1161 1
0.1%
1166 1
0.1%
ValueCountFrequency (%)
3366 1
0.1%
2970 1
0.1%
2788 1
0.1%
2750 1
0.1%
2732 1
0.1%
2614 1
0.1%
2541 1
0.1%
2503 2
0.1%
2492 1
0.1%
2472 1
0.1%

Sprint 400m (s)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct73
Distinct (%)24.3%
Missing1629
Missing (%)84.4%
Infinite0
Infinite (%)0.0%
Mean88.521595
Minimum56
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.2 KiB
2024-02-17T15:25:12.470313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile61
Q174
median84
Q397
95-th percentile122
Maximum255
Range199
Interquartile range (IQR)23

Descriptive statistics

Standard deviation24.514969
Coefficient of variation (CV)0.27693772
Kurtosis15.822892
Mean88.521595
Median Absolute Deviation (MAD)12
Skewness2.9984547
Sum26645
Variance600.9837
MonotonicityNot monotonic
2024-02-17T15:25:12.611860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 14
 
0.7%
70 12
 
0.6%
75 12
 
0.6%
69 10
 
0.5%
76 9
 
0.5%
80 9
 
0.5%
97 9
 
0.5%
100 9
 
0.5%
110 8
 
0.4%
78 8
 
0.4%
Other values (63) 201
 
10.4%
(Missing) 1629
84.4%
ValueCountFrequency (%)
56 3
0.2%
57 1
 
0.1%
58 3
0.2%
59 2
 
0.1%
60 5
0.3%
61 4
0.2%
62 1
 
0.1%
63 7
0.4%
64 2
 
0.1%
65 1
 
0.1%
ValueCountFrequency (%)
255 1
0.1%
240 2
0.1%
180 1
0.1%
160 1
0.1%
149 1
0.1%
139 1
0.1%
138 1
0.1%
135 1
0.1%
132 1
0.1%
130 1
0.1%

Interactions

2024-02-17T15:25:04.798213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:45.629016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.906955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.644210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.892090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.184056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.457269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.674935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.854643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.988151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.099410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.388651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.542496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.510326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.883732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:45.736535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:47.002132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.734762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.981326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.277329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.539920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.761074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.933437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.064930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.195858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.473608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.632565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.593049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.974526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:45.834766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:47.095510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.825356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.078562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.372941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.625337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.848954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.021440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.142894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.290768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.562884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.722097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.689662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.056192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:45.924566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:47.182974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.909152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.167011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.461667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.715769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.926829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.106722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.226957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.392436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.640783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.820466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.787003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.139590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.011970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:48.699136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.002616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.249303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.560305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.804069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.019526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.196371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.301670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.491218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.723429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.918170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.889736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.232462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.111092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:48.795805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.095002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.348048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.655545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.906747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.107503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.273517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.385282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.591225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.813089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:01.012284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.989955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.316364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.199512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:48.881698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.182629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.432452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.742376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.986805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.189734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.359040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.460858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.677641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.895800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:01.097718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.079765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.399225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.287966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:48.968745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.262775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.522459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.829638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.072336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.271341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.435617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.545272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.769990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.979289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:01.181208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.168578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.469830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.372160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.059035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.347864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.613246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.910110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.158048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.344120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.519807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.620177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.853784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.052194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:01.271747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.240440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.561331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.450351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.134815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.427867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.687648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.988163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.233294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.427272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.602390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.692713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.929051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.125262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:01.349576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.316848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.651465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.553923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.248391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.529214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.787953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.086786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.320651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.517678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.687312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.772917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.026531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.207859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.143079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.406636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.733772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.638435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.343483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.606813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.894701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.170046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.403416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.594432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.763789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.846521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.104522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.291764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.238556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.483572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.809637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.733730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.438787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.704443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:51.994785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.267420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.488326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.681993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.844670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:57.928865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.199260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.376666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.336625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.571465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:05.904830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:46.815206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:49.539173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:50.804019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:52.092769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:53.365890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:54.587349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:55.768392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:56.918631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:58.010868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:24:59.295858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:00.459590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:03.426773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-17T15:25:04.697800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-17T15:25:12.722360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Back Squat (lbs)Chad1000x (s)Clean and Jerk (lbs)Deadlift (lbs)Fight Gone BadFilthy 50 (s)Fran (s)Grace (s)Helen (s)L1 Benchmark (s)Max Pull-upsRankRun 5k (s)Snatch (lbs)Sprint 400m (s)
Back Squat (lbs)1.0000.0800.8260.8030.411-0.266-0.483-0.565-0.2831.0000.309-0.438-0.1530.766-0.205
Chad1000x (s)0.0801.000-0.121-0.043-0.2790.824-0.0160.0690.1520.0000.049-0.1350.292-0.0970.719
Clean and Jerk (lbs)0.826-0.1211.0000.7530.498-0.317-0.587-0.673-0.3621.0000.427-0.567-0.2150.906-0.261
Deadlift (lbs)0.803-0.0430.7531.0000.455-0.219-0.450-0.570-0.2921.0000.271-0.393-0.1160.672-0.100
Fight Gone Bad0.411-0.2790.4980.4551.000-0.508-0.500-0.559-0.464NaN0.404-0.509-0.4220.462-0.444
Filthy 50 (s)-0.2660.824-0.317-0.219-0.5081.0000.5180.3970.6041.000-0.4820.4510.405-0.3030.243
Fran (s)-0.483-0.016-0.587-0.450-0.5000.5181.0000.5740.6541.000-0.6860.6180.463-0.5950.324
Grace (s)-0.5650.069-0.673-0.570-0.5590.3970.5741.0000.4701.000-0.3800.4810.346-0.6300.252
Helen (s)-0.2830.152-0.362-0.292-0.4640.6040.6540.4701.0001.000-0.5530.4890.620-0.3570.530
L1 Benchmark (s)1.0000.0001.0001.000NaN1.0001.0001.0001.0001.000-1.0001.0001.0001.0001.000
Max Pull-ups0.3090.0490.4270.2710.404-0.482-0.686-0.380-0.553-1.0001.000-0.469-0.3210.461-0.353
Rank-0.438-0.135-0.567-0.393-0.5090.4510.6180.4810.4891.000-0.4691.0000.436-0.5780.348
Run 5k (s)-0.1530.292-0.215-0.116-0.4220.4050.4630.3460.6201.000-0.3210.4361.000-0.2410.608
Snatch (lbs)0.766-0.0970.9060.6720.462-0.303-0.595-0.630-0.3571.0000.461-0.578-0.2411.000-0.237
Sprint 400m (s)-0.2050.719-0.261-0.100-0.4440.2430.3240.2520.5301.000-0.3530.3480.608-0.2371.000

Missing values

2024-02-17T15:25:06.062268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-17T15:25:06.340930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-17T15:25:06.583088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
74767Andrea NislerCrossFit East NashvilleNaNworldwideWomen17.0worldwideopen350.00000260.00000400.0000205.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74796Chloe Gauvin-DavidCrossFit Move Fast Lift HeavyNaNworldwideWomen37.0worldwideopen280.00000230.00000350.0000190.00000NaN60.0NaNNaNNaN133.0106.0NaN1350.0NaN
74815Emelie LundbergCrossFit OBANaNworldwideWomen51.0worldwideopen319.66990231.48510374.7854198.41580NaNNaNNaNNaNNaN114.0NaNNaNNaNNaN
74818Jessica CoughlanCrossFit NorWestNaNworldwideWomen55.0worldwideopenNaNNaNNaNNaNNaN50.0NaNNaNNaN128.0143.0495.0NaNNaN
74823Astrid Tind PetersenCrossFit Butcher's LabNaNworldwideWomen105.0worldwideopen297.62370235.89434330.6930191.80194NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74824Devyn KimCrossFit InvictusNaNworldwideWomen118.0worldwideopenNaN225.00000315.0000175.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
74825Brooke Haas PeragineCrossFit FairhopeNaNworldwideWomen140.0worldwideopen350.00000250.00000375.0000202.00000336.075.0NaNNaN1199.0179.074.0558.01353.078.0
74826Madeleine HarrisCrossFit AylesburyNaNworldwideWomen142.0worldwideopen277.78212229.28048341.7161178.57422NaN48.0NaNNaNNaN170.0112.0NaN1292.0NaN
74827Lena RichterCrossFit OsloNaNworldwideWomen147.0worldwideopen315.26066253.53130363.7623198.41580NaNNaNNaNNaNNaNNaNNaNNaN1327.0NaN
74828Eugenie Amanda GosselinCrossFit LumberYardNaNworldwideWomen153.0worldwideopen280.00000225.00000330.0000185.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
AthleteAffiliateCountryRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)
76739Sarah NobleFountain City CrossFitNaNworldwideWomen108671.0worldwideopen155.0000085.0000175.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
76740Stephanie LaCosteGeaux CrossFitNaNworldwideWomen109187.0worldwideopen225.00000135.0000205.0000NaNNaNNaNNaNNaNNaNNaN256.0NaNNaNNaN
76741Catie DorisCamelback CrossFitNaNworldwideWomen109949.0worldwideopenNaNNaN200.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
76742Holly KrellTrain Harder CrossFitNaNworldwideWomen111358.0worldwideopen225.00000135.0000265.000095.0000NaNNaNNaNNaNNaNNaNNaNNaN2492.0NaN
76743Kerry RadiganSouthern Pines CrossFitNaNworldwideWomen112296.0worldwideopen165.00000100.0000NaN75.0000NaN5.0NaNNaNNaN858.0NaNNaNNaNNaN
76744Katelyn RyanCrossFit JaxNaNworldwideWomen112806.0worldwideopenNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2614.0NaN
76745Leah SmallwoodCrossFit WrexhamNaNworldwideWomen113774.0worldwideopen116.8448677.1617187.392755.1155199.0NaNNaNNaNNaNNaN250.0NaN1768.0NaN
76746Raine RyeNaNNaNworldwideWomen115832.0worldwideopen160.00000140.0000240.0000125.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
76747Kate CaseyWarlock CrossFitNaNworldwideWomen121746.0worldwideopen260.00000197.0000300.0000150.0000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
76748Karli RawlinsNaNNaNworldwideWomen123110.0worldwideopenNaN135.0000210.0000NaNNaNNaNNaNNaNNaN427.0NaNNaNNaNNaN

Duplicate rows

Most frequently occurring

AthleteAffiliateRegionDivisionRankGames_LevelQualifierBack Squat (lbs)Clean and Jerk (lbs)Deadlift (lbs)Snatch (lbs)Fight Gone BadMax Pull-upsChad1000x (s)L1 Benchmark (s)Filthy 50 (s)Fran (s)Grace (s)Helen (s)Run 5k (s)Sprint 400m (s)# duplicates
0Analise LepkanichCrossfit Peak 180 AthleticsworldwideWomen14513.0worldwideopen240.0000205.00000300.0000177.0000220.0NaNNaNNaN1562.0187.0122.0841.0NaNNaN2
1Angelica GaitanCrossFit TANKAworldwideWomen14108.0worldwideopen187.3927147.70954286.6006110.2310NaN25.0NaNNaNNaN300.0NaNNaN1500.0NaN2
2Ashley BigenhoCrossFit SalisburyworldwideWomen80450.0worldwideopen285.0000170.00000335.0000105.0000NaNNaNNaNNaNNaNNaN178.0NaN2158.0NaN2
3Brittany SmithCrossFit Casco Bay UndauntedworldwideWomen83206.0worldwideopen220.0000135.00000345.0000115.0000270.0NaNNaNNaN1993.0281.0227.0704.0NaN107.02
4Brooklynn Van RynCrossFit Currie BarracksworldwideWomen49355.0worldwideopenNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1866.0NaN2
5Emily ReboulCrossFit EixampleworldwideWomen74581.0worldwideopen187.3927NaN231.4851NaNNaNNaNNaNNaNNaNNaNNaNNaN1561.0NaN2
6Kayla AllenJurassic CrossFitworldwideWomen34210.0worldwideopen175.0000135.00000280.0000105.0000276.013.0NaNNaN1330.0317.0228.0552.01782.0NaN2
7Kim VulperhorstCrossFit StedebroecworldwideWomen14813.0worldwideopen286.6006202.82504341.7161165.3465NaN32.0NaNNaNNaN409.0162.0NaN1596.093.02
8Liza Tulisano WaltersCrossFit Thin Blue LineworldwideWomen45129.0worldwideopen315.0000205.00000345.0000140.0000283.022.04338.0NaN1385.0213.0138.0568.0NaN100.02
9Mackenzie HoyCrossFit HoldfastworldwideWomen42715.0worldwideopen245.0000187.00000315.0000134.0000NaNNaNNaNNaNNaNNaNNaNNaN2100.0NaN2